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Received：19 September 2023，
Revised：2024-04-10，
Published：16 September 2024
移动端阅览
目的
2
神经辐射场（NeRF）可以为数字人、交互游戏等虚拟现实应用提供沉浸式环境。然而，现有神经辐射场算法往往依赖大量位置的全景图进行大规模室内场景重建，在稀疏全景图条件下的重建效果不佳。为此，提出了一种用于室内稀疏全景图的神经辐射场重建算法，以实现低开销、高质量的室内新视角合成。
方法
2
针对稀疏输入问题，本文首先设计了深度监督策略，以分配更多的采样点在物体表面，从而获取更精细的几何重建结构。然后，引入了未观测视角下的射线形变损失来增强射线约束，从而有效提升了稀疏输入下的室内场景重建质量。
结果
2
本文算法在多个室内全景数据集上与较新的神经辐射场重建算法进行了比较。在两幅Replica数据集全景图输入条件下，本文算法在峰值信噪比（peak signal-to-noise ratio，PSNR）指标上比基准算法提升了6%。即使在单幅PNVS数据集全景图输入条件下，本文算法在PSNR指标上也比基准算法提升了11%。
结论
2
实验结果表明，本文提出的神经辐射场重建算法能够从稀疏室内全景图中重建高质量场景，实现高真实感的新视角合成。
Objective
2
Neural radiance field （NeRF） account for a crucial technology used in the creation of immersive environments in various applications， including digital human simulations， interactive gaming， and virtual-reality property tours. These applications benefit considerably from the highly realistic rendering capabilities of NeRF， which can generate detailed and interactive 3D spaces. However， the reconstruction of NeRF typically necessitates to a dense set of multiview images of indoor scenes， which can be difficult to obtain. Current algorithms that address sparse image inputs often perform poorly in the accurate reconstruction of indoor scenes， which leads to less than optimal results. To overcome these challenges， we introduced a novel NeRF reconstruction algorithm specifically designed for sparse indoor panoramas. This algorithm enhances the reconstruction process via the improved allocation of sampling points and refinement of the geometric structure regardless of limited image data. In this manner， high-quality， realistic virtual environments can be synthesized from sparsely available indoor panoramas， which advances the potential applications of NeRF in various fields.
Method
2
Initially， our algorithm， which is specifically designed to focused on regions of lower latitude， implements a distortion-aware sampling strategy during the ray sampling phase. This strategic approach ensures the sampling of more rays from the central areas of the panorama， which are typically richer in terms of visual information and less distorted compared with the peripheral regions. Concentration on these areas we can attain a marked improvement in rendering quality because the algorithm can better capture the essential features and details of the scene. For the further improvement of the reconstruction process， especially in the case sparse image inputs， a panoramic depth estimation network was employed. This network generates a depth map that provides crucial information on the spatial arrangement of objects within a scene. With the estimated depth map， our algorithm incorporates a depth sampling auxiliary strategy and a depth loss supervision strategy. These strategies work in tandem to guide the learning process of the network. The depth sampling strategy allocated a considerable portion of the sampling points in a Gaussian distribution around the estimated depth. This targeted approach enables the network to further comprehension of nuanced understanding of object surfaces， which is essential for accurate scene reconstruction. During the testing phase， our algorithm adopted a coarse-to-fine sampling strategy that aligns with the principles of NeRF. This methodical approach ensures that the network can progressively refine its understanding of the scene， starting with a broad overview and gradually through zooming in on finer details. To maintain the color and depth accuracy throughout the training process， we integrated a depth loss function during the training phase. This function effectively limits the variance of sampling point distribution， which results in a focused and accurate rendering of the scene. In addition， we tackled the issue involving artifacts and improved geometry through the introduction of distortion loss for unobserved viewpoints. This loss function effectively constrains the distribution of unobserved rays in space， which results in realistic and visually pleasing renderings. Moreover， to address the low rendering speed in neural rendering， we developed a real-time neural rendering algorithm with two distinct stages. The first stage involves partitions the bounding box of the scene into a series of octree grids， with each grid’s density determined via its spatial location. This process enables the efficient management of the complexity of the scene， which ensures that the rendering process is optimized for speed and quality. Further screening these grids leads to the identification of octree leaf nodes， which are essential for reducing memory consumption and improving the performance. In the second stage， our algorithm leverages the network to predict the color values of leaf nodes from various viewing directions. Spherical harmonics are employed to accurately fit the colors， which guaranteed that the rendered scene is vibrant and true to life. By caching the network model as an octree structure， we enable real-time rendering， which is crucial for applications that demand a seamless and immersive experience. This approach not only substantially improves the rendering speed but also maintains high-quality results， which are essential for the creation of realistic virtual environments.
Result
2
We evaluated the effectiveness of our proposed algorithm on three panoramic datasets， including two synthetic datasets （i.e.， Replica and PNVS datasets） and one real dataset （i.e.， WanHuaTong dataset）. This diverse selection of datasets allowed for a thorough assessment of the algorithm’s performance under various conditions and complexities. Our evaluation outcomes illustrate the effectiveness of our algorithm and its superiority over existing reconstruction methods. Specifically， when tested on a Replica dataset with two panoramic images as input， our algorithm exhibited a considerable leap over the current state-of-the-art dense depth priors for NeRF （DDP-NeRF） algorithm. In addition， it achieved a 6% improvement in peak signal-to-noise ratio （PSNR） and an 8% reduction in root mean square error， which reflect improvements in image quality and accuracy. Moreover， our algorithm demonstrated an impressive rendering speed of 70 frames per second on the WanHuaTong dataset， which underscores its capability to handle real depth data with equal proficiency. The algorithm’s adaptability is further highlighted in scenarios with challenging panoramic images， such as top cropping and partial depth occlusion. Despite these obstacles， our method effectively recovered complete depth information， which showcases its robustness and reliability in practical applications.
Conclusion
2
We proposed a NeRF reconstruction algorithm for sparse indoor panoramas， which enables highly realistic rendering from any viewpoints within the scene. Through the implementation of a panoramic-based ray sampling strategy and depth supervision， the algorithm improved the geometric reconstruction quality by focusing on object surfaces. In addition， it incorporated a deformation loss for unobserved viewpoints， which strengthened ray constraints and elevated the reconstruction quality under sparse input conditions. Experimental validation of different panoramic datasets demonstrated that our algorithm outperforms current techniques in terms of color and geometry metrics. This condition leads to the creation of highly realistic novel views and supports of real-time rendering， with potential applications in indoor navigation， virtual reality house viewing， mixed-reality games， and digital human scene synthesis.
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